Labellerr exists to tackle the challenges of manual, costly data labeling for AI/ML teams. It solves problems like slow data prep, inconsistent quality, and inefficient project management. Through automated workflows, generative AI, and collaboration, it rapidly delivers high-quality training data, saving up to 10x in time and cost, with secure cloud integration.
Labellerr supports all data modalities, such as 2D images, videos, PDFs, texts, audio, and geospatial images.
Labellerr supports object detection, segmentation, classification, transcription, speaker diarization, captioning, and entity recognition type annotation.
Active learning-based auto labeling reduces manual effort and saves up to 60% of projects’ time and costs.
To train object detection models, a collaborative workflow management software integrates into data pipelines, automating manual processes and QA. This enables high-speed image processing with quality checks, reducing costs by up to 70% and accelerating the process 99x for any industry.